Term Paper: Harnessing of Unstructured Data in Radiology

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The theory behind this type of software for the mining of radiology reports is that a great deal of information is lost in the pictures and images themselves (Chapman, et al., 2011). When a report is "read" through the use of a computer that is mining data from it, the software program reads the language in the report itself. The program is able to make sense of the words, terms, sentences, and other information that is written in the report. However, there must also be information in the images themselves, as that is the area on which radiology is based. Being able to quickly access these pictures is very important, because they can provide extra insight into the disease or condition of the patient, which can make a significant difference in the speed and accuracy of the diagnosis and treatment (Chapman, et al., 2011).

Use and Intended Impact

The use of unstructured data mining is varied, but radiology reports are a very popular area in which it is seen. For consideration here is the use of NLP, RadLex, and AIM in the radiology field, based on the mining of unstructured data. There is a great deal of unstructured data in radiology reports, and much of it can provide a benefit to the radiologist and to the doctors who look over the report in order to make a diagnosis and determine what method of treatment would be the best choice (Chapman, et al., 2011; Johnson, et al., 1997). The goal would be to use software to mine all of that unstructured data and provide it so it could be used by anyone who read the radiology report (Chapman, et al., 2011). That would allow doctors to have information at a glance that they might not otherwise notice, and would also allow them to provide more information in patient records that could be viewed by other doctors (Weiss & Langlotz, 2008). Making clinical decisions requires all the information possible, and the use of unstructured data mining could provide a higher level of information that could lead to better diagnostic success and a higher chance of the right treatments for every patient who is seen by radiology.

There is a risk with this type of data collection, however, because of uncertainty issues that currently exist in its ability to translate correctly and efficiently all the time (Chapman, et al., 2011; Demner-Fushman, Chapman, & McDonald, 2009; Do, et al., 2013). For the use of software for unstructured data mining to be acceptable, that issue would have to be completely corrected and thoroughly tested out so that patients' lives and well-being were not being put at risk from incorrect translation. Doctors must be able to trust what they read on a chart or diagnostic report, regardless of whether it is provided by another medical professional or translated by a computer (Chapman, et al., 2011). With the mining of unsecured data, there is an excellent opportunity to collect more information that can help provide patients with the best care possible (Demner-Fushman, Chapman, & McDonald, 2009). As long as the unsecured data is collected and translated properly, there will be great benefits seen (Chapman, et al., 2011).

NLP

Using NLP will have an excellent benefit for radiology, provided the translation of any unstructured data that is mined is correct (Weiss & Langlotz, 2008). The major impact will be on the patients themselves, because they will be the ones who will really benefit from more data about their diagnosis and treatment that is provided to their doctors and other medical providers in a structured way. Unstructured data is unorganized data, and does not lend itself to helping to diagnose or treat a patient, no matter his or her illness. The structured data in radiology reports is what matters, and if NLP can mine unstructured data and turn it -- accurately -- into structured data, there will be a significant impact on the value to both doctors and patients (Chapman, et al., 2011; Torres, et al., 2012). This impact is very important for the field, since it can save lives along with helping doctors diagnose and treat even mild conditions that are causing difficulty for a patient (Chapman, et al., 2011; Demner-Fushman, Chapman, & McDonald, 2009). However, if NLP is not used correctly it could have a very negative impact on radiology and other areas of healthcare because of inaccurate information.

RadLex

All of the best features from the existing systems for terminology in radiology are incorporated into RadLex, but the software also fills in gaps that were critical to the unstructured data mining of radiology reports but that were missing in the other methods that were used. This is vitally important, as the goal is to reach a type or style of software that can be used for data mining and that can handle unstructured data as well as structured data and pictures. While most of the options for data mining are helpful, none of them fully address all of the issues faced by those who are attempting to collect all of the data a radiology report has to offer (Chapman, et al., 2011). RadLex is not perfect, but because it fills in the most critical gaps in the ability to mine data and because it provides a link between all of the previous options that were used for the creation of radiology reports and the mining of their information through software, it is an excellent choice for unstructured data mining.

Annotated Image Markup

The value of AIM is an important one for use in collecting unstructured data as it relates to the pictures included in a radiology report. It is necessary to know the value of those pictures, and to ensure that they are providing the proper information (Chapman, et al., 2011). Without pictures of the patient to help identify the disease or condition with which he or she is dealing, the radiology report does not provide as much value to the doctor. That is where AIM comes in, and where it can be seen to have the most importance for the medical field. Being able to collect information from the pictures in the report and have them be part of the record that can be read at any other medical institution where the patient may need assistance is vital to the quality of care the patient needs (Chapman, et al., 2011). None of the methods used to mine unstructured data from radiology reports are perfect, but there are many ways in which various methods can work together in order to provide a highly successful outcome.

Interaction with Other Topics and Themes

The use of data mining relates to a number of other topics and themes that are seen in the field of health informatics. When healthcare meets information systems, informatics are created (Chapman, et al., 2011). This area requires a lot of study, because there are many different subareas that lie within it. In order to see where data mining falls in the context of health informatics, it is important to take a look at some of the other issues seen. The computers and programs that are used to handle data mining are part of informatics (Chapman, et al., 2011). These computers are required, or it would not be possible for the mining of data to take place at the current level. In order to translate the information from radiology reports and mine unstructured data properly, computers are vital to the process (Gerstmair, et al., 2012; Hong, et al., 2013). Computing power is increasing, and healthcare options are increasing as well. One of the ways patients can benefit from this is in the way they interact with their doctors and other healthcare providers. When information from a radiology report can be translated through data mining it can be made available electronically to doctors and medical personnel across the city, country, or world.

When data mining is used effectively, it can change the entire face of health informatics because it can use computers to provide doctors, radiologists, nurses, patients, and others with access to information they may have had to hunt for in the past (Weiss & Langlotz, 2008). That can affect how a person is treated and can also have an effect on the diagnosis the person receives. Getting the right diagnosis -- and getting it faster -- then affects the way the person receives treatment (Gerstmair, et al., 2012). Because quick treatment can be the difference between saving a life or failing in that regard, data mining has an important place in health informatics. Additionally, even less severe health problems can be mitigated, treated faster, and handled better when everything from the radiology report is available to everyone who needs the information (Chapman, et al., 2011; Do, et al., 2013).

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